






"""simple docstring"""


import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('0.8.3'):
		raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
		raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE   =   'The Nymphenburg Palace is a beautiful palace in Munich!'


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			{
						    'attention_cell': 'multi_head',
						    'num_layers': 4,
						    'units': 10_24,
						    'hidden_size': 7_68,
						    'max_length': 5_12,
						    'num_heads': 8,
						    'scaled': True,
						    'dropout': 0.1,
						    'use_residual': True,
						    'embed_size': 10_24,
						    'embed_dropout': 0.1,
						    'word_embed': None,
						    'layer_norm_eps': 1e-5,
						    'token_type_vocab_size': 2,
						}

						lowerCAmelCase   :List[Any]						=			bort_4_8_768_1024_hparams

						# Let's construct the original Bort model here
						# Taken from official BERT implementation, see:
						# https://github.com/alexa/bort/blob/master/bort/bort.py
						lowerCAmelCase   :Tuple						=			BERTEncoder(
						    attention_cell=predefined_args['attention_cell']     ,      num_layers=predefined_args['num_layers']     ,      units=predefined_args['units']     ,      hidden_size=predefined_args['hidden_size']     ,      max_length=predefined_args['max_length']     ,      num_heads=predefined_args['num_heads']     ,      scaled=predefined_args['scaled']     ,      dropout=predefined_args['dropout']     ,      output_attention=a__     ,      output_all_encodings=a__     ,      use_residual=predefined_args['use_residual']     ,      activation=predefined_args.get('activation'     ,      'gelu' )     ,      layer_norm_eps=predefined_args.get('layer_norm_eps'     ,      a__ )     ,      )

						# Vocab information needs to be fetched first
						# It's the same as RoBERTa, so RobertaTokenizer can be used later
						lowerCAmelCase   :Any						=			'openwebtext_ccnews_stories_books_cased'

						# Specify download folder to Gluonnlp's vocab
						lowerCAmelCase   :Dict						=			os.path.join(get_home_dir()     ,      'models' )
						lowerCAmelCase   :List[Any]						=			_load_vocab(a__     ,      a__     ,      a__     ,      cls=a__ )

						lowerCAmelCase   :Any						=			nlp.model.BERTModel(
						    a__     ,      len(a__ )     ,      units=predefined_args['units']     ,      embed_size=predefined_args['embed_size']     ,      embed_dropout=predefined_args['embed_dropout']     ,      word_embed=predefined_args['word_embed']     ,      use_pooler=a__     ,      use_token_type_embed=a__     ,      token_type_vocab_size=predefined_args['token_type_vocab_size']     ,      use_classifier=a__     ,      use_decoder=a__     ,      )

						original_bort.load_parameters(a__     ,      cast_dtype=a__     ,      ignore_extra=a__ )
						lowerCAmelCase   :List[str]						=			original_bort._collect_params_with_prefix()

						# Build our config 🤗
						lowerCAmelCase   :Optional[int]						=			{
						    'architectures': ['BertForMaskedLM'],
						    'attention_probs_dropout_prob': predefined_args['dropout'],
						    'hidden_act': 'gelu',
						    'hidden_dropout_prob': predefined_args['dropout'],
						    'hidden_size': predefined_args['embed_size'],
						    'initializer_range': 0.02,
						    'intermediate_size': predefined_args['hidden_size'],
						    'layer_norm_eps': predefined_args['layer_norm_eps'],
						    'max_position_embeddings': predefined_args['max_length'],
						    'model_type': 'bort',
						    'num_attention_heads': predefined_args['num_heads'],
						    'num_hidden_layers': predefined_args['num_layers'],
						    'pad_token_id': 1,  # 2 = BERT, 1 = RoBERTa
						    'type_vocab_size': 1,  # 2 = BERT, 1 = RoBERTa
						    'vocab_size': len(a__ ),
						}

						lowerCAmelCase   :str						=			BertConfig.from_dict(a__ )
						lowerCAmelCase   :Tuple						=			BertForMaskedLM(a__ )
						hf_bort_model.eval()

						# Parameter mapping table (Gluonnlp to Transformers)
						# * denotes layer index
						#
						# | Gluon Parameter                                                | Transformers Parameter
						# | -------------------------------------------------------------- | ----------------------
						# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
						# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
						# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
						# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
						# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
						# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
						# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
						# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
						# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
						# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
						# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
						# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
						# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
						# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

						# Helper function to convert MXNET Arrays to PyTorch
						def to_torch(a__ ) -> nn.Parameter:
												return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )

						# Check param shapes and map new HF param back
						def check_and_map_params(a__     ,      a__ ):
												lowerCAmelCase   :List[str]						=			hf_param.shape

												lowerCAmelCase   :Optional[int]						=			to_torch(params[gluon_param] )
												lowerCAmelCase   :str						=			gluon_param.shape

												assert (
												    shape_hf == shape_gluon
												), F"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

												return gluon_param

						lowerCAmelCase   :Union[str, Any]						=			check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight     ,      'word_embed.0.weight' )
						lowerCAmelCase   :str						=			check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight     ,      'encoder.position_weight' )
						lowerCAmelCase   :Tuple						=			check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias     ,      'encoder.layer_norm.beta' )
						lowerCAmelCase   :Optional[int]						=			check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.weight     ,      'encoder.layer_norm.gamma' )

						# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
						lowerCAmelCase   :str						=			torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

						for i in range(hf_bort_config.num_hidden_layers ):
												lowerCAmelCase   :BertLayer						=			hf_bort_model.bert.encoder.layer[i]

												# self attention
												lowerCAmelCase   :BertSelfAttention						=			layer.attention.self

												lowerCAmelCase   :Union[str, Any]						=			check_and_map_params(
												    self_attn.key.bias.data     ,      F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias""" )

												lowerCAmelCase   :List[Any]						=			check_and_map_params(
												    self_attn.key.weight.data     ,      F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight""" )
												lowerCAmelCase   :List[str]						=			check_and_map_params(
												    self_attn.query.bias.data     ,      F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias""" )
												lowerCAmelCase   :Tuple						=			check_and_map_params(
												    self_attn.query.weight.data     ,      F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight""" )
												lowerCAmelCase   :int						=			check_and_map_params(
												    self_attn.value.bias.data     ,      F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias""" )
												lowerCAmelCase   :List[str]						=			check_and_map_params(
												    self_attn.value.weight.data     ,      F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight""" )

												# self attention output
												lowerCAmelCase   :BertSelfOutput						=			layer.attention.output

												lowerCAmelCase   :int						=			check_and_map_params(
												    self_output.dense.bias     ,      F"""encoder.transformer_cells.{i}.proj.bias""" )
												lowerCAmelCase   :List[str]						=			check_and_map_params(
												    self_output.dense.weight     ,      F"""encoder.transformer_cells.{i}.proj.weight""" )
												lowerCAmelCase   :Tuple						=			check_and_map_params(
												    self_output.LayerNorm.bias     ,      F"""encoder.transformer_cells.{i}.layer_norm.beta""" )
												lowerCAmelCase   :List[Any]						=			check_and_map_params(
												    self_output.LayerNorm.weight     ,      F"""encoder.transformer_cells.{i}.layer_norm.gamma""" )

												# intermediate
												lowerCAmelCase   :BertIntermediate						=			layer.intermediate

												lowerCAmelCase   :List[str]						=			check_and_map_params(
												    intermediate.dense.bias     ,      F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias""" )
												lowerCAmelCase   :int						=			check_and_map_params(
												    intermediate.dense.weight     ,      F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight""" )

												# output
												lowerCAmelCase   :BertOutput						=			layer.output

												lowerCAmelCase   :Any						=			check_and_map_params(
												    bert_output.dense.bias     ,      F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias""" )
												lowerCAmelCase   :List[str]						=			check_and_map_params(
												    bert_output.dense.weight     ,      F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight""" )
												lowerCAmelCase   :Union[str, Any]						=			check_and_map_params(
												    bert_output.LayerNorm.bias     ,      F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta""" )
												lowerCAmelCase   :List[str]						=			check_and_map_params(
												    bert_output.LayerNorm.weight     ,      F"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma""" )

						# Save space and energy 🎄
						hf_bort_model.half()

						# Compare output of both models
						lowerCAmelCase   :Union[str, Any]						=			RobertaTokenizer.from_pretrained('roberta-base' )

						lowerCAmelCase   :Any						=			tokenizer.encode_plus(a__ )['input_ids']

						# Get gluon output
						lowerCAmelCase   :Any						=			mx.nd.array([input_ids] )
						lowerCAmelCase   :Union[str, Any]						=			original_bort(inputs=a__     ,      token_types=[] )

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(a__ )
						lowerCAmelCase   :List[Any]						=			BertModel.from_pretrained(a__ )
						hf_bort_model.eval()

						lowerCAmelCase   :Tuple						=			tokenizer.encode_plus(a__     ,      return_tensors='pt' )
						lowerCAmelCase   :Optional[int]						=			hf_bort_model(**a__ )[0]

						lowerCAmelCase   :Optional[int]						=			output_gluon[0].asnumpy()
						lowerCAmelCase   :str						=			output_hf[0].detach().numpy()

						lowerCAmelCase   :Optional[Any]						=			np.max(np.abs(hf_layer - gluon_layer ) ).item()
						lowerCAmelCase   :int						=			np.allclose(a__     ,      a__     ,      atol=1e-3 )

						if success:
												print('✔️ Both model do output the same tensors' )
						else:
												print('❌ Both model do **NOT** output the same tensors' )
												print('Absolute difference is:'     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""


import math


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if 0 not in (x, y):
												# We use the relation x^y = y*log10(x), where 10 is the base.
												return y * math.logaa(a__ )
						else:
												if x == 0:  # 0 raised to any number is 0
																		return 0
												elif y == 0:
																		return 1  # any number raised to 0 is 1
						raise AssertionError('This should never happen' )


if __name__ == "__main__":  # Main function
		# Read two numbers from input and typecast them to int using map function.
		# Here x is the base and y is the power.
		__SCREAMING_SNAKE_CASE   =   'Enter the base and the power separated by a comma: '
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   map(int, input(prompt).split(','))
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   map(int, input(prompt).split(','))

		# We find the log of each number, using the function res(), which takes two
		# arguments.
		__SCREAMING_SNAKE_CASE   =   res(xa, ya)
		__SCREAMING_SNAKE_CASE   =   res(xa, ya)

		# We check for the largest number
		if resa > resa:
				print('Largest number is', xa, '^', ya)
		elif resa > resa:
				print('Largest number is', xa, '^', ya)
		else:
				print('Both are equal')